The major problem addressed in this proposal is the development and evaluation of a noninvasive ultrasonic diagnostic approach for mapping a set of unique and quantitative ultrasonic metrics for liver disease. These are anticipated to differentiate physiological from pathological liver microanatomy in patients with diverse liver diseases with the use of conventional ultrasound scanners and a novel signal processing methodology. The method is based on radiofrequency signal (RF) processing schemes referred to as information theoretic detectors (ITD's) that have been shown to detect pathological changes in muscle diseases and cancer by measuring selected signal entropies. In this analysis of ultrasound data, we seek to describe the fundamental information content and the exact organization of scattering structures depicted within the backscattered ultrasound data. All of our present results have been obtained without the need for attenuation correction, and by real-time capable algorithms. These unique attributes of entropy analysis suggest that the automated processing we propose would be particularly robust for discrimination of deep tissues in a clinical environment. Most importantly, these techniques work on individual radio frequency A-Lines available from single element transducers and thus may be translated to the clinic without the need for relatively expensive imaging systems. As prevalence of liver disease increases with age (e.g. non alcoholic fatty liver disease: 9.7% among children, 26% among people 40-59 years old) the availability of a rapid, low-cost, point-of-care device for longitudina diagnosis of liver pathologies would find widespread clinical application supporting the health and well-being of older adults.